Recent Advances in Collaborative Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 4465

Special Issue Editors


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Guest Editor
Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain
Interests: safety; human factors; perception; manufacturing; human robot; interaction
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain
Interests: robot manipulation; navigation; software architecture; AI; safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Twenty-five years after Professors J. Edward Colgate and Michael Peshkin patented a robot architecture called the cobot, intended for direct physical interaction with human operators, one could argue that not all the expectations they created have been met. In fact, in most industrial applications today, cobots and human workers share the same space but complete tasks independently or sequentially—that is, they implement coexisting or sequential collaboration modes according to the collaboration levels defined by IFR, but cooperation or responsive collaboration are less common. It is not uncommon to find cobots inside physical fences due to incorrect needs analysis or false expectations of what cobots can offer.

However, the advances made have allowed the market to grow steadily, with established companies such as Universal Robot having sold more than 50,000 units since 2008, all robot brands including at least one cobot model in their portfolio, and many new cobot manufacturing companies appearing in the market. Analysts forecast 20% growth to continue until 2030.

This has been possible thanks to the shared efforts of scientists from different fields, standardization and regulatory bodies and industrial companies that have enabled advances in human–robot interaction, perception and cognition technologies, and the application of AI, new sensors and robot concepts to create safe environments, innovative control paradigms and regulatory frameworks. All this has led to the deployment of many real working applications in manufacturing, healthcare, service, etc.

This Special Issue aims to provide an overview of the latest developments in these fields, including examples of practical implementation.

Dr. Iñaki Maurtua
Dr. Ander Ansuategui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • interaction
  • control
  • safety
  • sensors
  • mechatronics
  • human factors
  • ethics
  • applications

Published Papers (2 papers)

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Research

14 pages, 3456 KiB  
Article
Online Cartesian Compliance Shaping of Redundant Robots in Assembly Tasks
by Branko Lukić, Kosta Jovanović, Leon Žlajpah and Tadej Petrič
Machines 2023, 11(1), 35; https://doi.org/10.3390/machines11010035 - 28 Dec 2022
Cited by 5 | Viewed by 1528
Abstract
This paper presents a universal approach to shaping the mechanical properties of the interaction between a collaborative robot and its environment through an end-effector Cartesian compliance shaping. More specifically, the focus is on the class of kinematically redundant robots, for which a novel [...] Read more.
This paper presents a universal approach to shaping the mechanical properties of the interaction between a collaborative robot and its environment through an end-effector Cartesian compliance shaping. More specifically, the focus is on the class of kinematically redundant robots, for which a novel redundancy reconfiguration scheme for online optimization of the Cartesian compliance of the end-effector is presented. The null-space reconfiguration aims to enable the more efficient and versatile use of collaborative robots, including robots with passive compliant joints. The proposed approach is model-based and gradient-based to enable real-time computation and reconfiguration of the robot for Cartesian compliance while ensuring accurate position tracking. The optimization algorithm combines two coordinate frames: the global (world) coordinate frame commonly used for end-effector trajectory tracking; and the coordinate frame fixed to the end-effector in which optimization is computed. Another attractive feature of the approach is the bound on the magnitude of the interaction force in contact tasks. The results are validated on a torque-controlled 7-DOF KUKA LWR robot emulating joint compliance in a quasi-static experiment (the robot exerts a force on an external object) and a peg-in-hole experiment emulating an assembly task. Full article
(This article belongs to the Special Issue Recent Advances in Collaborative Robotics)
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20 pages, 41403 KiB  
Article
Trajectory Planning of Dual-Robot Cooperative Assembly
by Xuyang Chen, Xiaojun You, Jinchao Jiang, Jinhua Ye and Haibin Wu
Machines 2022, 10(8), 689; https://doi.org/10.3390/machines10080689 - 13 Aug 2022
Cited by 9 | Viewed by 2471
Abstract
Efficiency can be improved through the cooperation of a dual-robot during assembly. However, how to effectively plan a simple and smooth path in a dynamic environment is a prominent problem in the process of dual-robot cooperative assembly. In this paper, a method based [...] Read more.
Efficiency can be improved through the cooperation of a dual-robot during assembly. However, how to effectively plan a simple and smooth path in a dynamic environment is a prominent problem in the process of dual-robot cooperative assembly. In this paper, a method based on RRT-Connect algorithm for trajectory planning and post-processing for trajectory optimization is proposed. This method takes full advantage of the excellent solution ability of RRT-Connect algorithm in the complex environment so as to obtain the initial path successfully. Through post-processing, the problem of RRT-Connect non-convergence to target is optimized. We use two 6-DOF industrial robots to build an experimental platform and design a dual-robot cooperative assembly system. According to the given task, the system can generate the original collision-free path based on RRT-Connect algorithm. Then the original path is simplified by Floyd algorithm and smoothed by multi-segment Bezier curve. Finally, the time parameter is sequenced for all the path points based on the iterative method, and the effective trajectory is obtained. The experimental results show that the algorithm proposed in this paper can effectively plan and optimize the trajectory of dual-robot. Compared to other methods, this approach has a higher success rate and less planning time. Full article
(This article belongs to the Special Issue Recent Advances in Collaborative Robotics)
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